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FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational characteristics of traffic flow types; by considering only the position of the selected bits from the packet header. The proposal a learning approach based on deep packet inspection which integrates both feature extraction and classification phases into one system. The results show that the FDPHI works very well on the applications of feature learning. Also, it presents powerful adequate traffic classification results in terms of energy consumption (70% less power CPU utilization around 48% less), and processing time (310% for IPv4 and 595% for IPv6).

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Publication Date
Tue Sep 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Two-Stage Classification of Breast Tumor Biomarkers for Iraqi Women
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Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

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Publication Date
Thu Sep 15 2022
Journal Name
Knowledge And Information Systems
Multiresolution hierarchical support vector machine for classification of large datasets
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Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

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Publication Date
Sat Jan 19 2019
Journal Name
Artificial Intelligence Review
Survey on supervised machine learning techniques for automatic text classification
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Publication Date
Sun Jan 01 2023
Journal Name
2nd International Conference On Mathematical Techniques And Applications: Icmta2021
Review of clustering for gene expression data
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Publication Date
Sat Dec 31 2022
Journal Name
Iraqi Geological Journal
Geochemical Criteria for Discriminating Shallow and Deep Environments in Oligocene-Miocene Succession, Western Iraq
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The geochemical study of the Oligocene-Miocene succession Anah, Euphrates, and Fatha formations, western Iraq, was carried out to discriminate their depositional environments. Different major and trace patterns were observed between these formations. The major elements (Ca, Mg, Fe, Mn, K, and Na) and trace elements (Li, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Zr, Cs, Ba, Hf, W, Pb, Th, and U) are a function of the setting of the depositional environments. The reefal facies have lower concentrations of MgO, Li, Cr, Co, Ni, Ga, Rb, Zr, and Ba than marine and lagoonal facies but have higher concentrations of CaO, V, and Sr than it. Whereas dolomitic limestone facies are enriched V, and U while depletion in Li, Cr, Ni, Ga, Rb, Sr, Zr, Ba, an

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Publication Date
Wed Jun 01 2022
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Fast discrimination of fake video manipulation
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<span>Deepfakes have become possible using artificial intelligence techniques, replacing one person’s face with another person’s face (primarily a public figure), making the latter do or say things he would not have done. Therefore, contributing to a solution for video credibility has become a critical goal that we will address in this paper. Our work exploits the visible artifacts (blur inconsistencies) which are generated by the manipulation process. We analyze focus quality and its ability to detect these artifacts. Focus measure operators in this paper include image Laplacian and image gradient groups, which are very fast to compute and do not need a large dataset for training. The results showed that i) the Laplacian

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Publication Date
Mon Jan 01 2007
Journal Name
2007 Ieee International Conference On Signal Processing And Communications
Fast Multi-level Image Vector Quantization
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Publication Date
Mon Sep 11 2017
Journal Name
Journal Of Mathematical Imaging And Vision
Fast Recursive Computation of Krawtchouk Polynomials
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Publication Date
Sat Jan 13 2018
Journal Name
Journal Of Engineering
Statistical Analysis of Mortality and Morbidity Due to Traffic Accidents in Iraq
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Undoubtedly, Road Traffic Accidents (RTAs) are a major dilemma in term of mortality and morbidity facing the road users as well as the traffic and road authorities. Since 2002, the population in Iraq has increased by 49 percent and the number of vehicles by three folds. Consequently, these increases were unfortunately combined with rising the RTAs number, mortality and morbidity. Alongside the humanitarian tragedies, every year, there are considerable economic losses in Iraq lost due to the epidemic of RTAs. Given the necessity of understanding the contributory factors related to RTAs for the implementation by traffic and road authorities to improve the road safety, the necessity have been a rise for

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Publication Date
Sat Jan 13 2018
Journal Name
Journal Of Engineering
Statistical Analysis of Mortality and Morbidity Due to Traffic Accidents in Iraq
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Undoubtedly, Road Traffic Accidents (RTAs) are a major dilemma in term of mortality and morbidity facing the road users as well as the traffic and road authorities. Since 2002, the population in Iraq has increased by 49 percent and the number of vehicles by three folds. Consequently, these increases were unfortunately combined with rising the RTAs number, mortality and morbidity. Alongside the humanitarian tragedies, every year, there are considerable economic losses in Iraq lost due to the epidemic of RTAs. Given the necessity of understanding the contributory factors related to RTAs for the implementation by traffic and road authorities to improve the road safety, the necessity have been a rise for this research which focuses into

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